Using participant data from the medical domain, the robustness of logistic regression (LR) with different cue inclusion levels and two fast and frugal (F&F) models in terms of predictive accuracy and frugality were tested. Two data sets based on judgments of verbally described patients were used: Heart failure (66 analysts), and Hyperlipidemia (38 analysts). In both data sets, when the models were cross-validated, there was a significant decrease in predictive accuracy for all models, especially when all cues were used in LR. The other models had about equal predictive accuracy, also when comparisons were made with actual diagnoses, with a slight advantage for LR in the Heart failure study. LR using the 5% inclusion level was more frugal than F&F. These results emphasize the importance of using cross-validation and of choosing the proper significance levels for cue inclusion and when comparing different judgment models.

It is assumed that in judgment analysis, experts provide better models than non-experts. In this study we challenge this view by showing that data from non-experts might be equally suitable for building models. We show this by modeling the decisions of 21 medical students, 27 general practitioners, and 22 cardiologists on real patient vignettes regarding diagnosing heart failure. The models used were logistic regression and fast and frugal models. Results showed that there were no differences between any of the expertise groups in terms of fit, prediction, information searched, or percent of actual diagnosis in any of the models. Therefore, it seems, at least for the studied conditions, using models built on decision data from non-experts versus experts might be equally valid in judgment analysis.

We present the Concordant-Ranks (CR) strategy that decision makers use to quickly find an alternative that is proximate to an ideal alternative in a multi-attribute decision space. CR implies that decision makers prefer alternatives that exhibit concordant ranks between attribute values and attribute weights. We show that, in situations where the alternatives are equal in multi-attribute utility (MAU), minimization of the weighted Euclidean distance (WED) to an ideal alternative implies the choice of a CR alternative. In two experiments, participants chose among, as well as evaluated, alternatives that were constructed to be equal in MAU. In Experiment 1, four alternatives were designed in such a way that the choice of each alternative would be consistent with one particular choice strategy, one of which was the CR strategy. In Experiment 2, participants were presented with a CR alternative and a number of arbitrary alternatives. In both experiments, participants tended to choose the CR alternative. The CR alternative was on average evaluated as more attractive than other alternatives. In addition, measures of WED, between given alternatives and the ideal alternative, by and large agreed with the preference order for choices and attractiveness evaluations of the different types of alternatives. These findings indicate that both choices and attractiveness evaluations are guided by proximity of alternatives to an ideal alternative.